External validation of the model for predicting high-grade patterns of stage ⅠA invasive lung adenocarcinoma based on clinical and imaging features
10.7507/1007-4848.202502042
- VernacularTitle:基于临床-影像特征预测ⅠA期浸润性肺腺癌高级别组织学亚型模型的外部验证
- Author:
Yu RONG
1
;
Nianqiao HAN
2
;
Yanbing HAO
3
;
Jianli HU
3
;
Yajin NIU
3
;
Lan ZHANG
4
;
Yuehua DONG
3
;
Nan ZHANG
5
;
Junfeng LIU
5
Author Information
1. 1. Department of Thoracic Surgery, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, Hebei, P. R. China 2. Department of Thoracic Surgery, The First Hospital of Hebei Medical University, Shijiazhuang, 050023, P. R. China
2. Department of Thoracic Surgery, Xiongan Xuanwu Hospital, Xiongan, 070001, Hebei, P. R. China
3. Department of Thoracic Surgery, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, Hebei, P. R. China
4. Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, Hebei, P. R. China
5. Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050031, P. R. China
- Publication Type:Journal Article
- Keywords:
Clinical prediction model;
lung adenocarcinoma;
high-grade pattern;
external validation
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2025;32(08):1096-1104
- CountryChina
- Language:Chinese
-
Abstract:
Objective To externally validate a prediction model based on clinical and CT imaging features for the preoperative identification of high-grade patterns (HGP), such as micropapillary and solid subtypes, in early-stage lung adenocarcinoma, in order to guide clinical treatment decisions. Methods This study conducted an external validation of a previously developed prediction model using a cohort of patients with clinical stage ⅠA lung adenocarcinoma from the Fourth Hospital of Hebei Medical University. The model, which incorporated factors including tumor size, density, and lobulation, was assessed for its discrimination, calibration performance, and clinical impact. Results A total of 650 patients (293 males, 357 females; age range: 30-82 years) were included. The validation showed that the model demonstrated good performance in discriminating HGP (area under the curve>0.7). After recalibration, the model's calibration performance was improved. Decision curve analysis (DCA) indicated that at a threshold probability>0.6, the number of HGP patients predicted by the model closely approximated the actual number of cases. Conclusion This study confirms the effectiveness of a clinical and imaging feature-based prediction model for identifying HGP in stage ⅠA lung adenocarcinoma in a clinical setting. Successful application of this model may be significant for determining surgical strategies and improving patients' prognosis. Despite certain limitations, these findings provide new directions for future research.
